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Pereira, I.

Publications and source records attributed to Pereira, I..

5 recordsLinked to original sources

Attenuated estrogen signaling disrupts placentation and drives trophoblast defects in Down syndrome

Proper establishment of the primitive placenta and subsequent tissue homeostasis in the mature placenta are critical for successful pregnancy in humans. Placental insufficiency is associated with adverse pregnancy outcomes, including fetal growth restriction, preeclampsia, and pregnancy loss. Moreover, emerging evidence suggests that placental defects are associated with long-term health challenges that manifest well into adulthood; yet the etiologies of such diseases are largely unknown. Defining the mechanistic basis for placental deficiencies, therefore, has important implications for improving both reproductive health and the lifelong well-being of affected children. Down syndrome is characterized by placental defects of unknown mechanistic origin, and notably, individuals with Down syndrome are at increased risk of developing diseases commonly associated with placental insufficiency later in life. Using induced pluripotent stem cells from Down syndrome patients, we found that stem cell-based embryo models (i.e., blastoids) and directed differentiation systems recapitulate trophoblast cell fate defects observed in placentas affected by Down syndrome. Furthermore, we demonstrate that attenuated estrogen signaling contributes to placental syncytialization defects and identify NRIP1, a transcriptional corepressor of estrogen receptor that is located on chromosome 21, as a key driver of trophoblast cell fate defects. Increased gene dosage of NRIP1 in an otherwise diploid cell line phenocopies cell fate defects observed in trophoblasts affected by Down syndrome. Our study suggests that estrogen signaling is a crucial regulator of trophoblast development and may serve as a potential target for therapeutic intervention. Highlights and eTOC blurbO_LIEstrogen signaling mediates syncytiotrophoblast fusion C_LIO_LIHuman iPS cells provide a tractable model for trophoblast defects in Down syndrome C_LIO_LITrophoblast differentiation and estrogen signaling are disrupted in Down syndrome C_LIO_LIIncreased NRIP1 expression is sufficient to induce trophoblast defects C_LI Logsdon and colleagues apply patient-derived induced pluripotent stem cells to recapitulate placentation defects observed in Down syndrome. The authors demonstrate that attenuated estrogen signaling disrupts trophoblast differentiation and identify NRIP1, a gene found on chromosome 21 that dampens estrogen signaling, as a regulator of trophoblast maturation. NRIP1 and estrogen signaling may represent important therapeutic targets for infertility and Down syndrome.

developmental biology↗

Temporal Transcriptomics Identifies Isoform-specific Trans-regulation by Multiple lncRNAs in Human iPSCs

Some long non-coding RNAs (lncRNAs) are known to regulate gene expression. However, the underlying temporal dynamics of lncRNAs influencing gene and epigenetic regulation and mechanisms of lncRNA regulation in trans are less understood. To investigate this, we genetically engineered 17 doxycycline-inducible lncRNA transgenes for ectopic expression at the H11 safe harbor locus in human pluripotent stem cells (hiPSCs), and we generated high-density temporal RNA-seq and ATAC-seq profiles. Most lncRNA transgenes were induced at 2 hours and maintained expression through the 96-hour time course. Surprisingly, when we sought to identify gene expression changes due to the lncRNAs, we found that the global transcriptional landscape was dominated by a strong systemic response triggered by doxycycline exposure. We rigorously defined this cohort of genes as a Doxycycline-Responsive Gene Signature (DRGS). The DRGS was also present in at least 28 public datasets from dox-inducible transgene studies involving diverse cell types. Next, we determined which lncRNAs exhibited trans-regulatory events. We identified DANCR, FENDRR, LINC00667, LINC00847, LNCPRESS1, and PNKY as lncRNAs that regulate specific transcript expression in trans. The downstream target genes encoded 53 mRNAs and 10 lncRNAs. None of the target lncRNAs altered gene expression proximal to their own loci (i.e., triggering secondary cis-effects). Surprisingly, the target genes of LINC00847 (transcribed from chromosome 22) were substantially enriched on chromosome 19, with a preponderance of target genes encoding RNA metabolism and RNA splicing factors. Collectively, our study provides a resource to discern artifacts in the doxycycline-inducible system and identifies temporally regulated targets of 6 lncRNAs for future mechanistic studies.

genomics↗

NeoDBS: Open-Source Platform for Visualization and Analysis of Electrophysiological Recordings from Deep Brain Stimulation Systems

Optimization of deep brain stimulation (DBS) therapy for neurological and neuropsychiatric disorders depends on objective quantitative biomarkers that can guide stimulation parameter adjustments. With the recent introduction of new-generation DBS systems capable of simultaneously stimulating brain activity and recording local field potentials (LFP), there is increasing demand for platforms that enable efficient visualization and analysis of these signals for electrophysiological biomarkers identification. To address the limitations of currently available toolboxes that require advanced signal processing skills and rely on proprietary software, we present NeoDBS, an open-source Python platform designed for ingestion and advance signal visualization and processing of LFP signals from DBS systems through an easy-to-use graphical interface. NeoDBS is a user-centered platform that offers predefined analysis pipelines with the aim of facilitating electrophysiological biomarker investigation for DBS across different brain disorders. Custom analysis pipelines are also available for users to leverage the signal analysis tools to their research needs. Critical functionalities for longitudinal biomarker research are featured in NeoDBS, such as batch file processing and event-locked analysis for in-clinic and at-home recordings. This combination of accessibility, user-experience and advanced signal processing tools makes NeoDBS an environment that propels easy and fast electrophysiological biomarker research for DBS, across patients, sessions, and stimulation parameters.

bioengineering↗

Determination of HLA-DR level in cytotoxic T lymphocytes: a new validated tool to predict breast cancer response to treatment

BackgroundNeoadjuvant chemotherapy (NACT) is the usual treatment for locally-advanced breast cancer (BC). However, more than half of the patients lack an effective response to this treatment. Thus, its urgent to find predictive biomarkers. Recently, we proposed the HLA-DR expression level in cytotoxic T lymphocytes (CTLs) as a robust biomarker to select, in advance, patients that will actually benefit from NACT. Patients and MethodsA total of 202 BC patients, 102 of which submitted to NACT, were enrolled in this study. 61 biopsies and 41 blood samples collected pre-NACT and 100 non-NACT tumor samples were immunophenotyped by flow cytometry. Both NACT and non-NACT patients were followed up for 34 months. Blood-isolated immune cells were cultured with BC cell lines in a 3D system. ResultsHere we confirmed that HLA-DR level in CTLs is a highly sensitive and specific biomarker to predict BC response to NACT, reflected in circulation and independent of the patients age, BC subtype and other tumor-immunological features. Therefore, we developed a predictive probability model, based on the determination of HLA-DR level in tumor-infiltrating CTLs, that could be used to guide therapeutic decisions. Interestingly, this biomarker was also associated with progression-free survival, regardless the treatment. Contrary to HLA-DRnegative CTLs, HLA-DR+ CTLs were able to reduce the viability of tumor cells, in culture, in agreement with their higher expression of activation, proliferation and cytotoxicity-related molecules. Tissue-residency and memory markers were also increased in HLA-DR+ CTLs. These anti-tumor features of HLA-DR+ CTLs may justify the clinical observations. ConclusionHLA-DR level in CTLs is a validated and independent biomarker to predict response to NACT which allow the establishment of a clinical meaningful tool to select in advance patients that will truly benefit from this treatment. Intriguingly, it may be further used as a biomarker of BC patients general prognosis. Highlights- HLA-DR level in cytotoxic T cells is an independent predictive factor of breast cancer response to NACT - A predictive probability model based on this biomarker was developed as a new tool to improve treatment decisions - HLA-DR level in cytotoxic T cells is also reflected systemically - HLA-DR level in cytotoxic T cells is also a prognostic factor, associated with progression-free survival - HLA-DR+ cytotoxic T cells exhibit several phenotypic and functional anti-tumor characteristics

immunology↗

TAPAS: an open-source software package for Translational Neuromodeling and Computational Psychiatry

Psychiatry faces fundamental challenges with regard to mechanistically guided differential diagnosis, as well as prediction of clinical trajectories and treatment response of individual patients. This has motivated the genesis of two closely intertwined fields: (i) Translational Neuromodeling (TN), which develops "computational assays" for inferring patient-specific disease processes from neuroimaging, electrophysiological, and behavioral data; and (ii) Computational Psychiatry (CP), with the goal of incorporating computational assays into clinical decision making in everyday practice. In order to serve as objective and reliable tools for clinical routine, computational assays require end-to-end pipelines from raw data (input) to clinically useful information (output). While these are yet to be established in clinical practice, individual components of this general end-to-end pipeline are being developed and made openly available for community use. In this paper, we present the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) software package, an open-source collection of building blocks for computational assays in psychiatry. Collectively, the tools in TAPAS presently cover several important aspects of the desired end-to-end pipeline, including: (i) tailored experimental designs and optimization of measurement strategy prior to data acquisition, (ii) quality control during data acquisition, and (iii) artifact correction, statistical inference, and clinical application after data acquisition. Here, we review the different tools within TAPAS and illustrate how these may help provide a deeper understanding of neural and cognitive mechanisms of disease, with the ultimate goal of establishing automatized pipelines for predictions about individual patients. We hope that the openly available tools in TAPAS will contribute to the further development of TN/CP and facilitate the translation of advances in computational neuroscience into clinically relevant computational assays.

neuroscience↗